Informatics Publishing Limited

G. Arumugam ¹, M. Thangaraj ¹, P. Shanthi ¹

Affiliations
1 Department of Computer Science, Madurai Kamaraj University, Madurai 625 021, Tamil Nadu, IN

Abstract

Data Mining, also known as knowledge discovery in databases, has been recognized as a promising new area for database research. Mining frequent item sets in transactional databases, binary transaction tables, time series databases and many other kinds of databases have been an active research topic over the past few years. Frequent access pattern is a special case of sequential pattern in an application database which helps to make effective decisions in the respective problem domain.

Given a large database of book transactions in the library, where each transaction consists of book-id, name of the book, author, and other related fields, the problem is to mine the frequent access patterns of the user from the library databases. The outcome of the findings will help the management to take effective steps that will cater the needs of the user.

Apriori and FP-growth algorithms can mine the complete sets of frequent item sets. These two algorithms were implemented and the performance of the algorithms was studied. The result shows that FP-growth algorithm performs well compared to Apriori.

Keywords

Digital Library, Access Patterns, Apriori, FP-Growth, Algorithm, Mining.

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M. Pushpa Rani ¹, G. Arumugam ²

Affiliations
1 Department of Computer Science, Mother Teresa Women’s University, Tamil Nadu, IN
2 Department of Computer Science, Madurai Kamaraj University, Madurai, IN

Abstract

Analyzing human gait has earned considerable interest among Computer Vision Community researchers as it has immense use in deducing the physical well-being of people. In this paper, a novel machine learning approach Extreme Learning Machine (ELM) normalized with T-Test is used to detect unusual gait patterns. Extreme Learning Machine classifiers are powerful tools, specifically designed to solve large-scale classification problems. In ELM, one may randomly choose and fix all the hidden node parameters and then analytically determine the output weights of Single-hidden Layer Feed forward neural Networks (SLFNs). After the hidden node parameters are chosen randomly, SLFN can be considered as a linear system and the output weights can be analytically determined through a generalized inverse operation of the hidden layer output matrices. ELM avoids problems like local minima, improper learning rate and over fitting which are commonly faced by the previous iterative learning methods. It also completes the training very fast. The multi category classification performance of ELM with T-Test and PCA are evaluated with Virginia Gait database. The results indicate that ELM produces better classification accuracy while reducing the system complexity and the training time.

Keywords

Extreme Learning Machine, SLFN, Gait Analysis, T-Test.

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M. Pushpa Rani ¹, G. Arumugam ²

Affiliations
1 Mother Teresa Women's University, Kodaikanal, Tamil Nadu, IN
2 Dept. of Computer Science, Madurai Kamaraj University, Madurai, Tamil Nadu, IN

Abstract

Biometric systems are becoming increasingly important, as they provide more reliable and efficient means of identity verification. Human identification at a distance has recently gained enormous interest among computer vision researchers. Gait recognition aims essentially to address this problem by recognising people based on the way they walk. In this paper, we propose an efficient self-similarity based gait recognition system for human identification using modified Independent Component Analysis (MICA). Initially the background modelling is done from a video sequence. Subsequently, the moving foreground objects in the individual image frames are segmented using the background subtraction algorithm. Then, the morphological skeleton operator is used to track the moving silhouettes of a walking figure. The MICA based on eigenspace transformation is then trained using the sequence of silhouette images. Finally, when a video sequence is fed, the proposed system recognizes the gait features and thereby humans, based on self-similarity measure. The proposed system is evaluated using gait databases and the experimentation on outdoor video sequences demonstrates that the proposed algorithm achieves a pleasing recognition performance.

Keywords

Gait Recognition, Modified Independent Component Analysis (MICA), Human detection and Tracking, Skeletonization, Morphological Operator.

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